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Whom You Know Matters: Relook Vehicle-to-Vehicle Communications from a Topological Perspective

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Computational Social Networks (CSoNet 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9197))

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Abstract

Vehicular communication networks such as vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) are a type of network in which vehicles and roadside units are the communicating nodes, providing each other with information such as safety warnings and traffic information. As a cooperative approach, vehicular communications can be more effective in avoiding accidents and traffic congestion than if each vehicle tries to solve these problems individually. Vehicular communications has some distinct characters such as fast moving, short-lived and opportunistic connections. Recently literature in this area is growing rapidly. The main focus in on studying how to advance and evaluate the traditional communication protocols and algorithms so as to be more effective in communicating information among those fast moving vehicles. Unfortunately there is far less work to reveal how the underlie connectivity of wireless network topological can affect the overlay communications behaviors. The vehicles’ communications behavior is not merely a function of message transmission or the wireless communications technologies, but also it is a network-wide role and organization. The wireless ties that link a vehicle to other vehicles are also a critical factor. In this paper, we are paving a new line of research on revealing the roles of network topological characters in vehicular communications. This novel research dimension is thought-provoking and opening a new conversation for researchers working in this area to rethink and redesign the communications protocols by also considering the topological connectivity related parameters.

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Correspondence to William Liu .

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Abbas, S.F., Liu, W., Bai, Q., Al-Anbuky, A. (2015). Whom You Know Matters: Relook Vehicle-to-Vehicle Communications from a Topological Perspective. In: Thai, M., Nguyen, N., Shen, H. (eds) Computational Social Networks. CSoNet 2015. Lecture Notes in Computer Science(), vol 9197. Springer, Cham. https://doi.org/10.1007/978-3-319-21786-4_22

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  • DOI: https://doi.org/10.1007/978-3-319-21786-4_22

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  • Online ISBN: 978-3-319-21786-4

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